from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI) benchmarks: perfect hyperparameters match

print_time_report()

Time report

sklearnex_KMeans_short: 0h 0m 1s

sklearnex_Ridge: 0h 0m 2s

KMeans_short: 0h 0m 2s

sklearnex_LogisticRegression: 0h 0m 4s

sklearnex_KMeans_tall: 0h 0m 7s

Ridge: 0h 0m 11s

LogisticRegression: 0h 0m 20s

KMeans_tall: 0h 0m 22s

sklearnex_KNeighborsClassifier_kd_tree: 0h 0m 28s

sklearnex_KNeighborsClassifier: 0h 2m 26s

KNeighborsClassifier_kd_tree: 0h 2m 38s

catboost_lossguide: 0h 5m 3s

xgboost: 0h 5m 8s

lightgbm: 0h 5m 11s

catboost_symmetric: 0h 5m 19s

HistGradientBoostingClassifier: 0h 5m 32s

KNeighborsClassifier: 0h 34m 5s

total: 1h 7m 7s

print_env_info()

Benchmark environment

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.8.0-1036-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.3",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.21.0",
    "scipy": "1.7.0",
    "Cython": null,
    "pandas": "1.3.0",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.132	0.0	6.066	0.0	-1	100	NaN	NaN	0.482	0.0	0.274	0.0	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.127	0.0	6.291	0.0	-1	5	NaN	NaN	0.468	0.0	0.272	0.0	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.128	0.0	6.245	0.0	1	1	NaN	NaN	0.468	0.0	0.274	0.0	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.128	0.0	6.258	0.0	1	100	NaN	NaN	0.458	0.0	0.279	0.0	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.123	0.0	6.485	0.0	-1	1	NaN	NaN	0.458	0.0	0.269	0.0	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	2	0	42	sklearn.datasets.make_classification	NaN	0.122	0.0	6.568	0.0	1	5	NaN	NaN	0.458	0.0	0.266	0.0	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.054	0.0	0.299	0.0	-1	100	NaN	NaN	0.096	0.0	0.557	0.0	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.064	0.0	0.250	0.0	-1	5	NaN	NaN	0.096	0.0	0.668	0.0	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.054	0.0	0.299	0.0	1	1	NaN	NaN	0.103	0.0	0.520	0.0	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.053	0.0	0.300	0.0	1	100	NaN	NaN	0.096	0.0	0.553	0.0	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.053	0.0	0.302	0.0	-1	1	NaN	NaN	0.096	0.0	0.550	0.0	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.053	0.0	0.300	0.0	1	5	NaN	NaN	0.097	0.0	0.552	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	35.370	0.000	0.0	0.035	-1	100	0.942	0.932	1.778	0.005	19.894	0.060	See	See
2	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.186	0.017	0.0	0.186	-1	100	1.000	1.000	0.086	0.000	2.171	0.201	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	34.789	0.000	0.0	0.035	-1	5	0.806	0.932	1.780	0.006	19.540	0.067	See	See
5	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.186	0.011	0.0	0.186	-1	5	1.000	1.000	0.088	0.006	2.106	0.185	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	12.670	0.026	0.0	0.013	1	1	0.702	0.828	1.788	0.144	7.087	0.572	See	See
8	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.195	0.001	0.0	0.195	1	1	1.000	1.000	0.085	0.000	2.283	0.018	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	21.539	0.082	0.0	0.022	1	100	0.942	0.691	1.734	0.004	12.422	0.055	See	See
11	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.205	0.001	0.0	0.205	1	100	1.000	1.000	0.087	0.000	2.362	0.016	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	26.128	0.338	0.0	0.026	-1	1	0.702	0.691	1.738	0.012	15.031	0.221	See	See
14	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.179	0.017	0.0	0.179	-1	1	1.000	1.000	0.086	0.000	2.083	0.203	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	2	0	42	sklearn.datasets.make_classification	NaN	21.335	0.026	0.0	0.021	1	5	0.806	0.828	1.738	0.004	12.279	0.035	See	See
17	KNeighborsClassifier	predict	1000000	1	100	2	0	42	sklearn.datasets.make_classification	NaN	0.203	0.001	0.0	0.203	1	5	1.000	1.000	0.086	0.000	2.373	0.010	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	34.394	0.000	0.0	0.034	-1	100	0.985	0.982	0.297	0.000	115.830	0.113	See	See
20	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.031	0.002	0.0	0.031	-1	100	1.000	1.000	0.005	0.000	6.016	0.628	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	34.443	0.000	0.0	0.034	-1	5	0.980	0.982	0.298	0.002	115.694	0.756	See	See
23	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.031	0.003	0.0	0.031	-1	5	1.000	1.000	0.005	0.000	6.094	0.747	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	10.072	0.004	0.0	0.010	1	1	0.973	0.982	0.251	0.000	40.103	0.070	See	See
26	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.014	0.001	0.0	0.014	1	1	1.000	1.000	0.005	0.000	2.753	0.251	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	21.017	0.019	0.0	0.021	1	100	0.985	0.974	0.250	0.001	83.915	0.254	See	See
29	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.022	0.001	0.0	0.022	1	100	1.000	1.000	0.005	0.000	4.507	0.465	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	23.586	0.151	0.0	0.024	-1	1	0.973	0.974	0.250	0.000	94.440	0.625	See	See
32	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.020	0.002	0.0	0.020	-1	1	1.000	1.000	0.005	0.000	3.977	0.506	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	20.905	0.053	0.0	0.021	1	5	0.980	0.982	0.251	0.001	83.222	0.282	See	See
35	KNeighborsClassifier	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.023	0.000	0.0	0.023	1	5	1.000	1.000	0.005	0.000	4.566	0.398	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.719	0.0	0.029	0.0	1	1	NaN	NaN	0.711	0.0	3.821	0.0	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.718	0.0	0.029	0.0	-1	5	NaN	NaN	0.713	0.0	3.813	0.0	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.719	0.0	0.029	0.0	-1	100	NaN	NaN	0.706	0.0	3.853	0.0	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.766	0.0	0.029	0.0	-1	1	NaN	NaN	0.704	0.0	3.928	0.0	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.791	0.0	0.029	0.0	1	100	NaN	NaN	0.724	0.0	3.852	0.0	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.799	0.0	0.029	0.0	1	5	NaN	NaN	0.704	0.0	3.975	0.0	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.708	0.0	0.023	0.0	1	1	NaN	NaN	0.429	0.0	1.648	0.0	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.708	0.0	0.023	0.0	-1	5	NaN	NaN	0.442	0.0	1.603	0.0	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.709	0.0	0.023	0.0	-1	100	NaN	NaN	0.473	0.0	1.501	0.0	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.692	0.0	0.023	0.0	-1	1	NaN	NaN	0.427	0.0	1.620	0.0	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.700	0.0	0.023	0.0	1	100	NaN	NaN	0.427	0.0	1.640	0.0	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.693	0.0	0.023	0.0	1	5	NaN	NaN	0.460	0.0	1.505	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	0.737	0.003	0.000	0.001	1	1	0.966	0.977	0.195	0.003	3.784	0.058	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.003	0.001	0.000	0.003	1	1	1.000	1.000	0.000	0.000	10.800	6.428	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	0.830	0.005	0.000	0.001	-1	5	0.976	0.978	0.574	0.003	1.447	0.012	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.006	0.002	0.000	0.006	-1	5	0.000	1.000	0.001	0.000	7.488	4.152	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	2.793	0.023	0.000	0.003	-1	100	0.973	0.960	0.106	0.001	26.336	0.381	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.012	0.002	0.000	0.012	-1	100	1.000	1.000	0.000	0.000	52.821	29.869	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	0.437	0.010	0.000	0.000	-1	1	0.966	0.978	0.571	0.001	0.766	0.017	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.004	0.001	0.000	0.004	-1	1	1.000	1.000	0.001	0.000	5.446	3.002	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	4.984	0.167	0.000	0.005	1	100	0.973	0.960	0.105	0.001	47.403	1.643	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.008	0.002	0.000	0.008	1	100	1.000	1.000	0.000	0.000	39.569	24.619	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2	0	42	sklearn.datasets.make_classification	NaN	1.457	0.007	0.000	0.001	1	5	0.976	0.977	0.196	0.002	7.448	0.073	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	2	0	42	sklearn.datasets.make_classification	NaN	0.004	0.001	0.000	0.004	1	5	0.000	1.000	0.000	0.000	15.638	9.957	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.021	0.001	0.001	0.000	1	1	0.963	0.985	0.001	0.000	19.033	4.606	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.640	4.747	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.024	0.001	0.001	0.000	-1	5	0.974	0.982	0.006	0.001	3.871	0.499	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	18.473	13.737	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.040	0.002	0.000	0.000	-1	100	0.975	0.975	0.001	0.000	53.370	20.632	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	21.041	15.749	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.023	0.001	0.001	0.000	-1	1	0.963	0.982	0.006	0.001	3.699	0.460	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	19.436	15.196	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.043	0.004	0.000	0.000	1	100	0.975	0.975	0.001	0.000	56.550	22.799	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.741	4.598	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	2	0	42	sklearn.datasets.make_classification	NaN	0.022	0.001	0.001	0.000	1	5	0.974	0.985	0.001	0.000	20.827	6.148	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	2	0	42	sklearn.datasets.make_classification	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.224	4.752	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.609	0.0	0.788	0.0	k-means++	NaN	30	NaN	0.385	0.0	1.584	0.0	See	See
3	KMeans_tall	fit	1000000	1000000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.474	0.0	1.012	0.0	random	NaN	30	NaN	0.397	0.0	1.193	0.0	See	See
6	KMeans_tall	fit	1000000	1000000	100	42	20.0	200	sklearn.datasets.make_blobs	30	6.583	0.0	3.646	0.0	k-means++	NaN	30	NaN	2.592	0.0	2.539	0.0	See	See
9	KMeans_tall	fit	1000000	1000000	100	42	20.0	200	sklearn.datasets.make_blobs	30	5.891	0.0	4.074	0.0	random	NaN	30	NaN	2.744	0.0	2.147	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.001	0.0	0.359	0.000	k-means++	0.001	30	0.001	0.0	0.0	9.391	6.219	See	See
2	KMeans_tall	predict	1000000	1	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.0	0.0	11.265	8.074	See	See
4	KMeans_tall	predict	1000000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.001	0.0	0.357	0.000	random	0.001	30	0.001	0.0	0.0	9.380	6.745	See	See
5	KMeans_tall	predict	1000000	1	2	42	20.0	200	sklearn.datasets.make_blobs	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.0	0.0	10.835	7.043	See	See
7	KMeans_tall	predict	1000000	1000	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	14.718	0.000	k-means++	0.002	30	0.002	0.0	0.0	6.326	3.437	See	See
8	KMeans_tall	predict	1000000	1	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.001	0.0	0.019	0.001	k-means++	1.000	30	1.000	0.0	0.0	10.853	7.467	See	See
10	KMeans_tall	predict	1000000	1000	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.002	0.0	15.450	0.000	random	0.002	30	0.002	0.0	0.0	5.921	2.966	See	See
11	KMeans_tall	predict	1000000	1	100	42	20.0	200	sklearn.datasets.make_blobs	30	0.001	0.0	0.019	0.001	random	1.000	30	1.000	0.0	0.0	10.877	7.840	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.207	0.0	0.015	0.0	k-means++	NaN	20	NaN	0.027	0.0	7.693	0.0	See	See
3	KMeans_short	fit	10000	10000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.071	0.0	0.045	0.0	random	NaN	20	NaN	0.080	0.0	0.890	0.0	See	See
6	KMeans_short	fit	10000	10000	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.550	0.0	0.291	0.0	k-means++	NaN	20	NaN	0.112	0.0	4.904	0.0	See	See
9	KMeans_short	fit	10000	10000	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.186	0.0	0.861	0.0	random	NaN	20	NaN	0.286	0.0	0.650	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_random_state	dataset_cluster_std	dataset_centers	dataset_sample_generator	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.002	0.0	0.185	0.000	k-means++	-0.002	20	-0.000	0.000	0.0	3.465	0.765	See	See
2	KMeans_short	predict	10000	1	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.001	0.0	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	10.328	7.989	See	See
4	KMeans_short	predict	10000	1000	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.002	0.0	0.187	0.000	random	0.001	20	0.001	0.000	0.0	3.447	0.708	See	See
5	KMeans_short	predict	10000	1	2	42	20.0	200	sklearn.datasets.make_blobs	20	0.001	0.0	0.000	0.001	random	1.000	20	1.000	0.000	0.0	10.766	7.474	See	See
7	KMeans_short	predict	10000	1000	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.002	0.0	6.743	0.000	k-means++	0.341	20	0.297	0.001	0.0	2.360	0.441	See	See
8	KMeans_short	predict	10000	1	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.001	0.0	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	9.133	5.286	See	See
10	KMeans_short	predict	10000	1000	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.002	0.0	6.685	0.000	random	0.370	20	0.337	0.001	0.0	2.425	0.409	See	See
11	KMeans_short	predict	10000	1	100	42	13.0	200	sklearn.datasets.make_blobs	20	0.001	0.0	0.012	0.001	random	1.000	20	1.000	0.000	0.0	9.018	5.028	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_informative	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	5	5	0	42	sklearn.datasets.make_classification	[20]	11.348	0.0	[-0.10397258]	0.000	NaN	NaN	NaN	NaN	NaN	1.930	0.0	5.880	0.0	See	See
3	LogisticRegression	fit	1000	1000	10000	5	5	0	42	sklearn.datasets.make_classification	[27]	0.810	0.0	[-2.63517582]	0.001	NaN	NaN	NaN	NaN	NaN	0.913	0.0	0.887	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_classes	dataset_n_informative	dataset_n_redundant	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	5	5	0	42	sklearn.datasets.make_classification	[20]	0.000	0.0	[57.94972429]	0.0	NaN	NaN	NaN	NaN	0.562	0.000	0.0	0.809	0.460	See	See
2	LogisticRegression	predict	1000000	1	100	5	5	0	42	sklearn.datasets.make_classification	[20]	0.000	0.0	[0.24686558]	0.0	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.374	0.367	See	See
4	LogisticRegression	predict	1000	100	10000	5	5	0	42	sklearn.datasets.make_classification	[27]	0.002	0.0	[139.64539121]	0.0	NaN	NaN	NaN	NaN	0.280	0.003	0.0	0.557	0.121	See	See
5	LogisticRegression	predict	1000	1	10000	5	5	0	42	sklearn.datasets.make_classification	[27]	0.000	0.0	[25.98221181]	0.0	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.125	0.102	See	See

Ridge: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_informative	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	10	42	sklearn.datasets.make_regression	NaN	0.174	0.0	0.461	0.0	NaN	NaN	NaN	0.174	0.0	0.997	0.0	See	See
3	Ridge	fit	1000000	1000000	100	10	42	sklearn.datasets.make_regression	NaN	1.462	0.0	0.547	0.0	NaN	NaN	NaN	0.225	0.0	6.497	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	dataset_n_informative	dataset_random_state	dataset_sample_generator	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	10	42	sklearn.datasets.make_regression	NaN	0.011	0.0	7.098	0.0	NaN	NaN	0.11	0.018	0.0	0.624	0.017	See	See
2	Ridge	predict	1000	1	10000	10	42	sklearn.datasets.make_regression	NaN	0.000	0.0	1.151	0.0	NaN	NaN	NaN	0.000	0.0	0.649	0.717	See	See
4	Ridge	predict	1000000	1000	100	10	42	sklearn.datasets.make_regression	NaN	0.000	0.0	5.748	0.0	NaN	NaN	1.00	0.000	0.0	0.621	0.510	See	See
5	Ridge	predict	1000000	1	100	10	42	sklearn.datasets.make_regression	NaN	0.000	0.0	0.014	0.0	NaN	NaN	NaN	0.000	0.0	0.647	0.746	See	See